Privacy

Don't really know what to publish on the entire Facebook & Cambridge Analytica
clusterfuck. On one hand, if you use Facebook you have it
coming.
On the other hand, I still think Cambridge Analytica "powers of persuasion",
if you want to call them that way, have been blown out of proportion.

Tech

Artificial intelligence is already making significant inroads in taking
over mundane, time-consuming tasks many humans would rather not do. The
responsibilities and consequences of handing over work to AI vary greatly,
though; some autonomous systems recommend music or movies; others recommend
sentences in court. Even more advanced AI systems will increasingly control
vehicles on crowded city streets, raising questions about safety—and about
liability, when the inevitable accidents occur.

I was recently chatting to a friend whose startup’s machine learning
models were so disorganized it was causing serious problems as his team
tried to build on each other’s work and share it with clients. Even the
original author sometimes couldn’t train the same model and get similar
results! He was hoping that I had a solution I could recommend, but I had to
admit that I struggle with the same problems in my own work. It’s hard to
explain to people who haven’t worked with machine learning, but we’re still
back in the dark ages when it comes to tracking changes and rebuilding
models from scratch. It’s so bad it sometimes feels like stepping back in
time to when we coded without source control.

Machine learning can drive tangible business value for a wide range of
industries — but only if it is actually put to use. Despite the many machine
learning discoveries being made by academics, new research papers showing
what is possible, and an increasing amount of data available, companies are
struggling to deploy machine learning to solve real business problems. In
short, the gap for most companies isn’t that machine learning doesn’t work,
but that they struggle to actually use it.